Artificial Intelligence (AI) vs. Machine Learning (ML) vs. & Deep Learning (DL): What’s the difference?
These 3 terms Artificial intelligence, and deep learning and machine learning are related to the modern computer technology. The term “Artificial Intelligence”, is well known and very familiar and thanks to the movies like The Terminator, ex Machina etc. as they focus this term in these movies and make many people clear about AI.
But recently Machine learning and deep learning are also becoming popular, and they are sometimes interchangeably with artificial intelligence, so that’s why the main differences between these three terms are very unclear and unknown. In this blog, we discuss some basic points about these 3 terms (Artificial intelligence, Deep learning, and machine learning). We will also discuss that how they are different from each other? Artificial Intelligence: This word is the combination of 2 words “Artificial” (made by human or non-natural) and “intelligence” (capability of thinking or understanding). Artificial intelligence (AI) is the well-known technology that is common in all three.
This is a misconception that artificial intelligence is a system basically, it is not a system but it is implemented within the system to enhance the working and functionality. You can say that it is the study to train or learn the computers so that the computer can do those things even better than humans. It is intelligence that the modern science wants to implement in the system to add human capabilities to a machine.
From SIRI to self-driving cars, AI is progressing rapidly, while science fictions portray artificial intelligence with human-like characteristics. AI is also known as weak AI because it is designed with the intent to perform the narrow work or task like (only car driving or only facial recognition). Modern science is working to develop strong artificial intelligence because Narrow artificial intelligence performs the specific task like playing a chess or solve an equation while strong AI would perform at nearly every Cognitive human task.
AI Applications: Artificial intelligence is now getting very popular in the health section to fast diagnoses than human and reducing patient cost. The well-known artificial intelligence healthcare technology is IBM Watson. It is used in business to learn algorithms that are integrated into (system analytics and CRM platforms) to serve customers in the best way.
AI tutors are now adopted by our education system also and AI tutor can provide the support and help to students to stay on track. It is also applied to the finance apps like TurboTax etc. for best results. It can help to collect personal data and provides the best advice. AI has been followed in the manufacturing area and this is the forefront of robots in the workflow. Industrial robots are used to perform specific sensitive tasks and they are kept separated from human workers.
Machine learning is also a learning technique in which machine can learn on its own, its mean that it can work without being programmed or code. It is the artificial intelligence that can provide the ability to the systems to automatically learn like humans. In this way, the experience can be improved.
ML involves building mathematical models for the purpose of understanding data. When we give these models changeable or adjustable parameters, it can be accommodated by observed data, and it is considered that data is learned by this. Once these mathematical models have been fit to previously seen data, newly observed data can also predict or understand.
Machine learning focuses on the computer program or code and then learn by itself. Machine learning is said to be learning like the human brain. Machine learning is just a method of achieving artificial intelligence.
Methods/Types of machine learning:
There are four most common types of machine learning. Here we study the different types of machine learning.
Supervised machine learning: This type of learning is used when data is labeled. It is majorly used by practical machines. An algorithm is used to learn the mapping functionalities from input to the output. It can also make predictions and assumptions about output clues after learning. It is able to provide new input after training. Moreover, it can also check the output with the correct results, find the bugs and errors and update the models accurately.
Unsupervised machine learning: When the trained information is not labeled or classified, this algorithm is used. It studies how a system can judge a function to describe the structure of hidden and unlabeled information or data. The system correct output but it can drive assumptions and results from datasets by exploring data.
Semi-supervised machine learning: This type of machine learning fall somewhere between the supervised and unsupervised ML because it uses the labeled (small amount) and unlabeled (large amount) both for training. This type of system is used to enhance the accuracy and reliability of outputs.
Reinforcement machine learning: This type of machine learning directly interacts with the environment around by discovering errors and producing actions. Error searching & delayed reward are the most similar properties of reinforcement learning. In this learning, machine and software determine the behavior automatically for efficient performance.
Machine learning enables data analysis, delivers faster and error-free results to identify risk or opportunities, but it may need more time and proper resources to learn and train properly. Learning with artificial intelligence might make it a perfect choice to process large data volumes.
Deep learning is the approach of machine learning. The machine learning techniques that teach the computers to react like humans or what comes naturally to humans. For example, the technology behind the driverless auto vehicles, recognizing traffic signs or to find a pedestrian on the roadside is deep learning.
It is also behind the voice controls of different devices and it is getting much attention and it really deserves it because it is achieving the results that are not possible before. This technology is inspired by brain structure (interconnections of neurons). In deep learning, the computer learns to perform tasks from text, sound or images.
Its big achievement is an art of accuracy that even AI has not done yet. Artificial intelligence, DL, and ML are different terms as their learning techniques are different than each other. AI is the unique term but deep learning is one of the common approaches to machine learning.
These terms seem to be closely related with one another, because of their applications in modern computer sciences. But, there is a difference between these terms. AI is implemented into the system to enhance the functionalities of the system while machine and deep learning can learn to react naturally like humans by its own without programming codes.